It is common for industrial and commercial facilities to operate a large number of machines such as electrical motors concurrently, many of which may cooperate in a large interdependent process or system. Despite increasingly efficient maintenance programs, at any time some percentage of the machines develop defects that are likely to lead to machine failure. For example, machines having moving parts (e.g., bearings) and experience constant friction that results in wear. It is known that bearing failures are a major cause of motor faults. Bearing damage due to wear may not be apparent absent gross damage or failure of the motor, however, because the bearing's wear site is likely concealed in the motor's assembled state.
Consequently, the use of machine condition monitoring systems has become essential to preventive maintenance of industrial machinery in order to avoid down time or catastrophic failure of machines. Unscheduled plant shutdowns can result in considerable financial losses. Failure of high performance machinery can lead to fatal injury and processing system backup. Typical benefits from a preventive maintenance program include longer periods between machinery shutdowns, evaluation of the condition of machine components without resorting to costly and/or destructive disassembly for visual inspection, and prolonging the machinery's operational life by taking corrective action when developing faults are identified early.
Measurement and analysis of machine vibrations typically includes sensing the machine's vibrations with a transducer that converts the vibration information to electrical signals. The electrical signals are processed so that a history of vibration amplitude over time can be obtained. Data points representing amplitude at a certain point in time may be plotted on a graph of amplitude versus time. This graph is often referred to as the time-domain vibration signature of the machine. FIG. 1 shows an exemplary graph of time-domain vibration data. FIG. 1 is a plot of measured acceleration of a point of a machine assembly over a period of about eight seconds. The particular machine from which this data was measured was rotating at 104.98 rpm, so FIG. 1 shows data over the course of about 15 revolutions. Peak values measured were about 0.025 g.
Rotating and reciprocating components of a machine produce vibrations having a wide range of frequencies. In addition to the time-domain data representation of machine vibrations, the vibrations of a machine, machine component, or other phenomena acting on the machine may be characterized by a plot of vibration energy as a function of vibration frequency. This diagram is commonly referred to as a “frequency spectrum,” “spectral diagram,” or “spectrum plot.” FIG. 2 shows an exemplary frequency spectrum, which was derived from the time-domain vibration data of FIG. 1. Although the frequency scale is not illustrated in FIG. 2, prominent peaks are seen at about 10-11 Hz (designated as peak 10) and about 87 Hz (designated as peak 20).
The frequencies and associated peaks of the vibrations of a specific machine collectively make up the “frequency spectrum” for the machine, also known as the machine's “vibration signature.” A machine's vibration signature varies with, for example, the design, manufacture, application, and wear of its components. The machine's normal operating conditions can determine the amplitude of steady (or “normal”) vibration. It is a common practice to obtain a reference frequency spectrum when the machine is known to be in good condition for comparison against future measurements of the machine's frequency spectrum. Such comparison aids in detecting changes in the condition of the machine or its subcomponents. Hence, analysis of a machine's vibration signature provides valuable insights into the condition of the machine. Monitoring systems may include one or more sensors mounted on the machine and configured to measure a performance characteristic of the machine, such as vibration, temperature, pressure, etc. and as discussed in U.S. Pat. No. 7,289,919 to Boerhout hereby incorporated by reference in its entirety. Often, each machine has multiple sensors mounted at various locations on the machine, which may all be of the same type or different types. When different types of sensors are employed, each sensor type may use a measurement technique that differs from the other sensor types.
Further, the sensors may send data continuously to a connected central processing unit (i.e., hard-wired or wireless) or may periodically transmit data to a hand-held measuring device that is temporarily connected with the sensors. Such a hand-held unit may process the data to provide performance information (e.g., vibration level) directly to a user or may merely store the data for subsequent transfer to a separate processing device. The hand-held unit or other wireless vibration sensors may be battery powered and may consume substantial power during the acquisition, transmission, and processing steps. The transmission step may consume the most power.
There is a need to reduce the amount of power used during the process of sending, receiving, processing, or the like steps when machine data may be analyzed for monitoring systems to provide a more efficient system. The present invention is directed to energy efficient machine monitoring systems and methods along with data compression systems and methods, which may even be on-machine, to provide efficient use of sensor equipment during the acquisition, transmission, and/or processing steps of a machine's vibrations or other data.